Online Feedback Efficient Active Target Discovery in Partially Observable Environments

TL;DR

This paper introduces DiffATD, a diffusion-guided active target discovery method that efficiently balances exploration and exploitation in partially observable environments, outperforming baselines without prior supervised training.

Contribution

The paper presents a novel diffusion-guided approach for active target discovery that operates without supervised training and offers interpretability in partially observable settings.

Findings

DiffATD outperforms baseline methods across multiple domains.

It effectively balances exploration and exploitation.

It achieves competitive results with fully supervised methods.

Abstract

In various scientific and engineering domains, where data acquisition is costly--such as in medical imaging, environmental monitoring, or remote sensing--strategic sampling from unobserved regions, guided by prior observations, is essential to maximize target discovery within a limited sampling budget. In this work, we introduce Diffusion-guided Active Target Discovery (DiffATD), a novel method that leverages diffusion dynamics for active target discovery. DiffATD maintains a belief distribution over each unobserved state in the environment, using this distribution to dynamically balance exploration-exploitation. Exploration reduces uncertainty by sampling regions with the highest expected entropy, while exploitation targets areas with the highest likelihood of discovering the target, indicated by the belief distribution and an incrementally trained reward model designed to learn the characteristics of the target. DiffATD enables efficient target discovery in a partially observable environment within a fixed sampling budget, all without relying on any prior supervised training. Furthermore, DiffATD offers interpretability, unlike existing black--box policies that require extensive supervised training. Through extensive experiments and ablation studies across diverse domains, including medical imaging, species discovery, and remote sensing, we show that DiffATD performs significantly better than baselines and competitively with supervised methods that operate under full environmental observability.